Personalized recommendation in social tagging systems using hierarchical clustering

Shepitsen, Andriy; Gemmell, Jonathan; Mobasher, Bamshad; Burke, Robin

doi:10.1145/1454008.1454048

Cited by 428 publications

(249 citation statements)

References 11 publications

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“…In recommender system research, extensive studies is going on to take advantage of tags for recommendations [7,8]. Single systems like Delicious or Flickr offer recommendations to their users based on their data and also researchers take advantage of single Web2.0 services to create recommender systems [9].…”

Section: Related Workmentioning

confidence: 99%

ReMashed – Recommendations for Mash-Up Personal Learning Environments

Drachsler

Pecceu

Arts

et al. 2009

Lecture Notes in Computer Science

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Abstract.The following article presents a Mash-Up Personal Learning Environment called ReMashed that recommends learning resources from emerging information of a Learning Network. In ReMashed learners can specify certain Web2.0 services and combine them in a Mash-Up Personal Learning Environment. Learners can rate information from an emerging amount of Web2.0 information of a Learning Network and train a recommender system for their particular needs. ReMashed therefore has three main objectives: 1. to provide a recommender system for Mash-up Personal Learning Environments to learners, 2. to offer an environment for testing new recommendation approaches and methods for researchers, and 3. to create informal user-generated content data sets that are needed to evaluate new recommendation algorithms for learners in informal Learning Networks.

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Section: Related Workmentioning

confidence: 99%

ReMashed – Recommendations for Mash-Up Personal Learning Environments

Drachsler

Pecceu

Arts

et al. 2009

Lecture Notes in Computer Science

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“…The tags entered in the system allow users to freely explore objects and other users' profiles without having to follow a rigid predefined hierarchy of concepts [14].…”

Section: Related Workmentioning

confidence: 99%

Social Tagging for e-Learning: An Approach Based on the Triplet of Learners, Learning Objects and Tags

Zaina

Júnior

Amaral

2015

Communications in Computer and Information Science

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Abstract. The emerging of the Web 2.0 has allowed users more interactivity with Web applications. Social tagging has been recognized as an important solution to the description of resources available on the Web. In the context of elearning it may be used as an auxiliary mechanism to the composition of learning object metadata. This paper presents an approach based on the triplet of learners, learning objects and tags for providing the social tagging for e-learning. We performed an experiment with 336 technician students that marked 218 electronic learning objects for about 4,985 times. Although our results have shown that social tagging is a promising practice for e-learning some challenges on how to implement it has to be overcome.

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“…A personalized retrieval model that exploits user profiles defined in a folksonomy has been investigated in previous approaches [9,6,12]. Shepitsen et al applied a hierarchical clustering algorithm to the tags associated to a user profile, defined in Delicious [9].…”

Section: Related Workmentioning

confidence: 99%

“…Shepitsen et al applied a hierarchical clustering algorithm to the tags associated to a user profile, defined in Delicious [9]. They used the generated tag clusters to provide personalized item recommendations.…”

Section: Related Workmentioning

confidence: 99%

“…Apart from facilitating the organization and sharing of content, these 'social tagging' actions can be a fairly accurate source user interests. Several studies have proven that a user profile can be effectively harvested from these tagging services [1,10], and later exploited on different personalization services, such as tag recommendation [3], item recommendation [9], and personalized search [6,9,12], to name a few.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting Social Tagging Profiles to Personalize Web Search

Vallet

Cantador

Jose

2009

Flexible Query Answering Systems

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract. In this paper, we investigate the exploitation of user profiles defined in social tagging services to personalize Web search. One of the key challenges of a personalization framework is the elicitation of user profiles able to represent user interests. We propose a personalization approach that exploits the tagging information of users within a social tagging service as a way of obtaining their interests. We evaluate this approach in Delicious, a social Web bookmarking service, and apply our personalization approach to a Web search system. Our evaluation results indicate a clear improvement of our approach over related state of the art personalization approaches.

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Personalized recommendation in social tagging systems using hierarchical clustering

Cited by 428 publications

References 11 publications

ReMashed – Recommendations for Mash-Up Personal Learning Environments

ReMashed – Recommendations for Mash-Up Personal Learning Environments

Social Tagging for e-Learning: An Approach Based on the Triplet of Learners, Learning Objects and Tags

Exploiting Social Tagging Profiles to Personalize Web Search

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